feat: add streaming output
This commit is contained in:
qixinbo
+137
-132
@@ -375,130 +375,141 @@ Language: Chinese (Simplified)
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{"role": "user", "content": user_prompt}
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]
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# 5. Call LLM
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try:
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llm_started = time.perf_counter()
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await emit_progress("正在生成 SQL")
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response = None
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last_error = ""
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for attempt in range(NL2SQL_LLM_RETRY_COUNT + 1):
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try:
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response = await asyncio.wait_for(
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provider.chat(
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messages=messages,
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max_tokens=NL2SQL_MAX_TOKENS,
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temperature=NL2SQL_TEMPERATURE,
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reasoning_effort=NL2SQL_REASONING_EFFORT,
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request_timeout=NL2SQL_LLM_REQUEST_TIMEOUT_SECONDS,
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num_retries=0,
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),
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timeout=NL2SQL_LLM_TIMEOUT_SECONDS,
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)
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except asyncio.TimeoutError:
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last_error = f"LLM generation timeout after {NL2SQL_LLM_TIMEOUT_SECONDS}s"
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if attempt < NL2SQL_LLM_RETRY_COUNT:
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await emit_progress(f"SQL 生成超时，正在重试 ({attempt + 1}/{NL2SQL_LLM_RETRY_COUNT})")
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continue
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return NL2SQLResponse(sql="", result=[], error=last_error)
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except Exception as e:
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last_error = f"LLM generation failed: {e}"
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if attempt < NL2SQL_LLM_RETRY_COUNT:
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await emit_progress(f"SQL 生成失败，正在重试 ({attempt + 1}/{NL2SQL_LLM_RETRY_COUNT})")
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continue
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return NL2SQLResponse(sql="", result=[], error=last_error)
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if response.finish_reason == "error":
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last_error = response.content or "LLM Error"
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if attempt < NL2SQL_LLM_RETRY_COUNT:
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await emit_progress(f"模型返回错误，正在重试 ({attempt + 1}/{NL2SQL_LLM_RETRY_COUNT})")
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continue
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return NL2SQLResponse(sql="", result=[], error=last_error)
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break
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if response is None:
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return NL2SQLResponse(sql="", result=[], error=last_error or "LLM generation failed")
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content = (response.content or "").strip()
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if not content:
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return NL2SQLResponse(sql="", result=[], error="LLM returned empty response")
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# Clean up code blocks
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if "```json" in content:
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content = content.split("```json")[1].split("```")[0]
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elif "```" in content:
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content = content.split("```")[1].split("```")[0]
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content = content.strip()
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# 5. Call LLM & 6. Execute SQL (with Self-Correction Loop)
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MAX_SQL_EXEC_RETRIES = int(os.getenv("NL2SQL_MAX_EXEC_RETRIES", "2"))
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sql_query = ""
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formatted_results = []
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chart_response = None
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timeout_stage = "llm_generation"
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for exec_attempt in range(MAX_SQL_EXEC_RETRIES + 1):
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try:
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result_json = json.loads(content)
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sql_query = result_json.get("sql", "").strip()
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except json.JSONDecodeError:
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# Fallback if LLM doesn't return valid JSON despite instructions
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sql_query = content
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await emit_progress(f"SQL 生成完成 ({time.perf_counter() - llm_started:.2f}s)")
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except Exception as e:
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return NL2SQLResponse(sql="", result=[], error=f"LLM generation failed: {e}")
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# 6. Execute SQL
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try:
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timeout_stage = "sql_execution"
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sql_exec_started = time.perf_counter()
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await emit_progress("正在执行 SQL 查询")
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if request.source == "upload":
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if upload_df is None:
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upload_payload = await asyncio.to_thread(_get_upload_payload, request.file_url)
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upload_df = upload_payload["df"]
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timeout_stage = "sql_execution"
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formatted_results = await asyncio.wait_for(
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asyncio.to_thread(_execute_upload_sql, sql_query, upload_df),
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timeout=NL2SQL_SQL_EXEC_TIMEOUT_SECONDS,
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)
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else:
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timeout_stage = "sql_execution"
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results = await asyncio.wait_for(
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asyncio.to_thread(connector.execute_query, sql_query),
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timeout=NL2SQL_SQL_EXEC_TIMEOUT_SECONDS,
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)
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# Format results
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formatted_results = []
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if isinstance(results, list):
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if results and isinstance(results[0], dict):
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formatted_results = results
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elif results and isinstance(results[0], (list, tuple)):
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# Handle tuple/list results (like ClickHouse withColumnTypes=False, or just in case)
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# If we have column info (ClickHouse withColumnTypes=True returns (result_rows, column_types))
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# But execute_query wrapper in ClickHouseConnector now returns (data, columns_with_types)
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# Wait, client.execute(with_column_types=True) returns (data, columns_with_types)
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# Let's check what connector.execute_query returns.
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# PostgresConnector returns list of dicts.
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# ClickHouseConnector (modified) returns (data, columns_with_types) OR just data if wrapper logic differs.
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# Let's handle the ClickHouse case explicitly if possible or make it generic.
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# If results is list of tuples/lists, we need headers.
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# Postgres returns list of dicts, so we are good.
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# ClickHouse: if modified to return client.execute(..., with_column_types=True),
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# it returns `(result_rows, column_types_list)`.
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# So `results` here would be a tuple, not a list.
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formatted_results = [list(row) for row in results]
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else:
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formatted_results = results
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elif isinstance(results, tuple) and len(results) == 2:
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# Likely ClickHouse (rows, columns)
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rows, cols = results
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col_names = [c[0] for c in cols]
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formatted_results = [dict(zip(col_names, row)) for row in rows]
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llm_started = time.perf_counter()
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if exec_attempt == 0:
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await emit_progress("正在生成 SQL")
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else:
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# Unknown format, try to return as is or empty
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formatted_results = []
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await emit_progress(f"SQL 执行完成，返回 {len(formatted_results)} 行 ({time.perf_counter() - sql_exec_started:.2f}s)")
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await emit_progress(f"正在尝试修复 SQL ({exec_attempt}/{MAX_SQL_EXEC_RETRIES})")
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# 7. Generate Chart
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chart_response = None
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if request.generate_chart and formatted_results:
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response = None
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last_error = ""
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for attempt in range(NL2SQL_LLM_RETRY_COUNT + 1):
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try:
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response = await asyncio.wait_for(
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provider.chat(
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messages=messages,
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max_tokens=NL2SQL_MAX_TOKENS,
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temperature=NL2SQL_TEMPERATURE,
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reasoning_effort=NL2SQL_REASONING_EFFORT,
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request_timeout=NL2SQL_LLM_REQUEST_TIMEOUT_SECONDS,
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num_retries=0,
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),
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timeout=NL2SQL_LLM_TIMEOUT_SECONDS,
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)
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except asyncio.TimeoutError:
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last_error = f"LLM generation timeout after {NL2SQL_LLM_TIMEOUT_SECONDS}s"
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if attempt < NL2SQL_LLM_RETRY_COUNT:
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await emit_progress(f"SQL 生成超时，正在重试 ({attempt + 1}/{NL2SQL_LLM_RETRY_COUNT})")
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continue
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return NL2SQLResponse(sql=sql_query, result=[], error=last_error)
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except Exception as e:
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last_error = f"LLM generation failed: {e}"
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if attempt < NL2SQL_LLM_RETRY_COUNT:
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await emit_progress(f"SQL 生成失败，正在重试 ({attempt + 1}/{NL2SQL_LLM_RETRY_COUNT})")
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continue
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return NL2SQLResponse(sql=sql_query, result=[], error=last_error)
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if response.finish_reason == "error":
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last_error = response.content or "LLM Error"
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if attempt < NL2SQL_LLM_RETRY_COUNT:
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await emit_progress(f"模型返回错误，正在重试 ({attempt + 1}/{NL2SQL_LLM_RETRY_COUNT})")
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continue
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return NL2SQLResponse(sql=sql_query, result=[], error=last_error)
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break
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if response is None:
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return NL2SQLResponse(sql=sql_query, result=[], error=last_error or "LLM generation failed")
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content = (response.content or "").strip()
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if not content:
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return NL2SQLResponse(sql=sql_query, result=[], error="LLM returned empty response")
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# Clean up code blocks
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if "```json" in content:
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content = content.split("```json")[1].split("```")[0]
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elif "```" in content:
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content = content.split("```")[1].split("```")[0]
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content = content.strip()
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try:
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result_json = json.loads(content)
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sql_query = result_json.get("sql", "").strip()
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except json.JSONDecodeError:
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# Fallback if LLM doesn't return valid JSON despite instructions
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sql_query = content
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await emit_progress(f"SQL 生成完成 ({time.perf_counter() - llm_started:.2f}s)")
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except Exception as e:
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return NL2SQLResponse(sql=sql_query, result=[], error=f"LLM generation failed: {e}")
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# 6. Execute SQL
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try:
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timeout_stage = "sql_execution"
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sql_exec_started = time.perf_counter()
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await emit_progress("正在执行 SQL 查询")
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if request.source == "upload":
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if upload_df is None:
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upload_payload = await asyncio.to_thread(_get_upload_payload, request.file_url)
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upload_df = upload_payload["df"]
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formatted_results = await asyncio.wait_for(
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asyncio.to_thread(_execute_upload_sql, sql_query, upload_df),
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timeout=NL2SQL_SQL_EXEC_TIMEOUT_SECONDS,
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)
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else:
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results = await asyncio.wait_for(
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asyncio.to_thread(connector.execute_query, sql_query),
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timeout=NL2SQL_SQL_EXEC_TIMEOUT_SECONDS,
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)
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# Format results
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formatted_results = []
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if isinstance(results, list):
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if results and isinstance(results[0], dict):
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formatted_results = results
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elif results and isinstance(results[0], (list, tuple)):
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formatted_results = [list(row) for row in results]
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else:
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formatted_results = results
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elif isinstance(results, tuple) and len(results) == 2:
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rows, cols = results
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col_names = [c[0] for c in cols]
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formatted_results = [dict(zip(col_names, row)) for row in rows]
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else:
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formatted_results = []
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await emit_progress(f"SQL 执行完成，返回 {len(formatted_results)} 行 ({time.perf_counter() - sql_exec_started:.2f}s)")
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break # Execution succeeded, break the retry loop
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except asyncio.TimeoutError:
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return NL2SQLResponse(sql=sql_query, result=[], error=f"SQL execution timeout after {NL2SQL_SQL_EXEC_TIMEOUT_SECONDS}s")
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except Exception as e:
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if exec_attempt < MAX_SQL_EXEC_RETRIES:
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await emit_progress(f"SQL 执行失败，准备自动修复 ({exec_attempt + 1}/{MAX_SQL_EXEC_RETRIES})")
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messages.append({"role": "assistant", "content": f"```json\n{{\"sql\": \"{sql_query}\"}}\n```"})
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messages.append({
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"role": "user",
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"content": f"The generated SQL failed to execute. Database error:\n{str(e)}\n\nPlease fix the SQL query to resolve this error and provide the corrected version following the exact same JSON format."
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})
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continue
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else:
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return NL2SQLResponse(sql=sql_query, result=[], error=f"SQL execution failed after {MAX_SQL_EXEC_RETRIES} retries: {e}")
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# 7. Generate Chart
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if request.generate_chart and formatted_results:
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try:
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chart_started = time.perf_counter()
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await emit_progress("正在生成可视化方案")
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timeout_stage = "chart_generation"
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@@ -506,16 +517,8 @@ Language: Chinese (Simplified)
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generate_chart(formatted_results, request.query),
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timeout=NL2SQL_CHART_TIMEOUT_SECONDS,
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)
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if not chart_response or not chart_response.chart_spec:
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# Do not fallback automatically if the LLM explicitly decided not to or failed.
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# Just pass whatever it returned (or lack thereof)
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pass
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await emit_progress(f"可视化方案生成完成 ({time.perf_counter() - chart_started:.2f}s)")
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await emit_progress(f"NL2SQL 总耗时 {time.perf_counter() - total_started:.2f}s")
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return NL2SQLResponse(sql=sql_query, result=formatted_results, chart=chart_response)
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except asyncio.TimeoutError:
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if timeout_stage == "chart_generation":
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except asyncio.TimeoutError:
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fallback_chart = ChartGenerationResponse(
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reasoning=f"Chart generation timeout after {NL2SQL_CHART_TIMEOUT_SECONDS}s",
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chart_type="",
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@@ -523,6 +526,8 @@ Language: Chinese (Simplified)
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chart_spec=None,
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)
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return NL2SQLResponse(sql=sql_query, result=formatted_results, chart=fallback_chart)
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return NL2SQLResponse(sql=sql_query, result=[], error=f"SQL execution timeout after {NL2SQL_SQL_EXEC_TIMEOUT_SECONDS}s")
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except Exception as e:
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return NL2SQLResponse(sql=sql_query, result=[], error=f"SQL execution failed: {e}")
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except Exception as e:
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pass # Ignore chart generation errors, return data only
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await emit_progress(f"NL2SQL 总耗时 {time.perf_counter() - total_started:.2f}s")
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return NL2SQLResponse(sql=sql_query, result=formatted_results, chart=chart_response)
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@@ -34,6 +34,8 @@ from nanobot.config.schema import Config
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from app.api.skills import load_skills
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from app.services.llm_cache import get_llm_configs
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from app.core.streaming_provider import StreamingLiteLLMProvider
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class NanobotIntegration:
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def __init__(self):
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self.agent: AgentLoop | None = None
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@@ -156,7 +158,7 @@ class NanobotIntegration:
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spec = find_by_name(provider_name)
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# Skip API key check for now to allow initialization without full config
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return LiteLLMProvider(
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return StreamingLiteLLMProvider(
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api_key=p.api_key if p else None,
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api_base=config.get_api_base(model),
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default_model=model,
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@@ -211,7 +213,7 @@ class NanobotIntegration:
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cached = self._model_agent_cache.get(model_id)
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if cached:
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return cached
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provider = LiteLLMProvider(
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provider = StreamingLiteLLMProvider(
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api_key=target_config.get("api_key"),
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api_base=target_config.get("api_base"),
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default_model=target_config.get("model"),
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@@ -0,0 +1,76 @@
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import contextvars
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import json
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from typing import Any, Dict, List, Optional
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from loguru import logger
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from nanobot.providers.litellm_provider import LiteLLMProvider
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from nanobot.providers.base import LLMResponse
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from litellm import acompletion, stream_chunk_builder
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streaming_queue_var = contextvars.ContextVar("streaming_queue", default=None)
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class StreamingLiteLLMProvider(LiteLLMProvider):
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async def chat(
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self,
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messages: List[Dict[str, Any]],
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tools: Optional[List[Dict[str, Any]]] = None,
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model: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 4000,
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reasoning_effort: Optional[str] = None,
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request_timeout: Optional[int] = None,
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num_retries: Optional[int] = None,
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) -> LLMResponse:
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original_model = model or self.default_model
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model_name = self._resolve_model(original_model)
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kwargs: Dict[str, Any] = {
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"model": model_name,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": True, # 强制开启流式
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}
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if self.api_key and self.api_key != "no-key":
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kwargs["api_key"] = self.api_key
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if self.api_base:
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kwargs["api_base"] = self.api_base
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if self.extra_headers:
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kwargs["extra_headers"] = self.extra_headers
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if tools:
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kwargs["tools"] = tools
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if request_timeout is not None:
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kwargs["timeout"] = request_timeout
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if num_retries is not None:
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kwargs["num_retries"] = max(0, int(num_retries))
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if reasoning_effort and self._supports_reasoning_effort(model_name):
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kwargs["reasoning_effort"] = reasoning_effort
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try:
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response_stream = await acompletion(**kwargs)
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chunks = []
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queue = streaming_queue_var.get()
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async for chunk in response_stream:
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chunks.append(chunk)
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if queue is not None:
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# 提取普通内容或 think 内容
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delta = chunk.choices[0].delta if chunk.choices else None
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if delta:
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content = getattr(delta, "content", None)
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reasoning_content = getattr(delta, "reasoning_content", None)
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if content:
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await queue.put({"type": "delta", "content": content})
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if reasoning_content:
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await queue.put({"type": "progress", "content": reasoning_content, "is_reasoning": True})
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# 还原为完整的 response 对象供 nanobot 处理
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full_response = stream_chunk_builder(chunks, messages=messages)
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return self._parse_response(full_response)
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except Exception as e:
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logger.error("StreamingLiteLLMProvider failed: {}", e)
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raise
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+11
-5
@@ -170,6 +170,8 @@ async def nanobot_chat(request: ChatRequest):
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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from app.core.streaming_provider import streaming_queue_var
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@app.post("/nanobot/chat/stream")
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async def nanobot_chat_stream(request: ChatRequest):
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async def event_generator():
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@@ -184,6 +186,8 @@ async def nanobot_chat_stream(request: ChatRequest):
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yield f"data: {json.dumps({'type': 'routing', 'selected': 'agent', 'reason': 'auto_routed_by_agent'}, ensure_ascii=False)}\n\n"
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|
||||
progress_queue: asyncio.Queue[str] = asyncio.Queue()
|
||||
# 设置 streaming_queue_var 为当前请求的 progress_queue
|
||||
streaming_queue_var.set(progress_queue)
|
||||
|
||||
async def _on_progress(content: str, **kwargs: Any) -> None:
|
||||
if content:
|
||||
@@ -237,7 +241,10 @@ async def nanobot_chat_stream(request: ChatRequest):
|
||||
break
|
||||
try:
|
||||
progress = await asyncio.wait_for(progress_queue.get(), timeout=0.2)
|
||||
yield f"data: {json.dumps({'type': 'progress', 'content': progress}, ensure_ascii=False)}\n\n"
|
||||
if isinstance(progress, dict):
|
||||
yield f"data: {json.dumps(progress, ensure_ascii=False)}\n\n"
|
||||
else:
|
||||
yield f"data: {json.dumps({'type': 'progress', 'content': progress}, ensure_ascii=False)}\n\n"
|
||||
except asyncio.TimeoutError:
|
||||
continue
|
||||
|
||||
@@ -266,10 +273,9 @@ async def nanobot_chat_stream(request: ChatRequest):
|
||||
session.messages[-1]["viz"] = viz_payload
|
||||
nanobot_service.agent.sessions.save(session)
|
||||
|
||||
for idx in range(0, len(text), STREAM_DELTA_CHUNK_SIZE):
|
||||
chunk = text[idx: idx + STREAM_DELTA_CHUNK_SIZE]
|
||||
yield f"data: {json.dumps({'type': 'delta', 'content': chunk}, ensure_ascii=False)}\n\n"
|
||||
|
||||
# Since true streaming is enabled via StreamingLiteLLMProvider,
|
||||
# we no longer need to chunk and yield `text` here.
|
||||
# Just yield the final text to signal completion and update final state.
|
||||
yield f"data: {json.dumps({'type': 'final', 'content': text}, ensure_ascii=False)}\n\n"
|
||||
yield f"data: {json.dumps({'type': 'done'}, ensure_ascii=False)}\n\n"
|
||||
except asyncio.CancelledError:
|
||||
|
||||
@@ -24,6 +24,7 @@ interface Message {
|
||||
viz?: MessageViz;
|
||||
progressLogs?: string[];
|
||||
routeInfo?: string;
|
||||
reasoningContent?: string;
|
||||
}
|
||||
|
||||
interface MessageViz {
|
||||
@@ -526,18 +527,26 @@ export function ChatInterface() {
|
||||
progressLogs: ["请求已提交，准备路由..."],
|
||||
}]);
|
||||
|
||||
const pushProgressLog = (text: string) => {
|
||||
if (!text.trim()) return;
|
||||
setMessagesForSession(targetSessionKey, (prev) =>
|
||||
prev.map((msg) => {
|
||||
if (msg.id !== assistantId) return msg;
|
||||
const pushProgressLog = (text: string, isReasoningToken: boolean = false) => {
|
||||
if (!text.trim() && !isReasoningToken) return;
|
||||
setMessagesForSession(targetSessionKey, (prev) =>
|
||||
prev.map((msg) => {
|
||||
if (msg.id !== assistantId) return msg;
|
||||
|
||||
if (isReasoningToken) {
|
||||
// 对于流式推理内容，拼接而不是创建新条目
|
||||
const currentReasoning = msg.reasoningContent || "";
|
||||
return { ...msg, reasoningContent: currentReasoning + text };
|
||||
} else {
|
||||
// 对于普通的阶段性日志，保留最近的 8 条
|
||||
const current = msg.progressLogs || [];
|
||||
if (current[current.length - 1] === text) return msg;
|
||||
const next = [...current, text].slice(-8);
|
||||
return { ...msg, progressLogs: next };
|
||||
})
|
||||
);
|
||||
};
|
||||
}
|
||||
})
|
||||
);
|
||||
};
|
||||
|
||||
const token = localStorage.getItem("token");
|
||||
const effectiveModelId = selectedModelId || currentModel?.id || "";
|
||||
@@ -627,6 +636,7 @@ export function ChatInterface() {
|
||||
const payload = JSON.parse(payloadText) as {
|
||||
type: string;
|
||||
content?: string;
|
||||
is_reasoning?: boolean;
|
||||
sql?: string;
|
||||
result?: unknown;
|
||||
error?: string;
|
||||
@@ -652,7 +662,9 @@ export function ChatInterface() {
|
||||
}
|
||||
|
||||
if (payload.type === "progress" && payload.content) {
|
||||
pushProgressLog(payload.content);
|
||||
// 如果 progress 内容带有空格或者换行，并且不是典型的系统提示词，很可能这是 reasoning_content
|
||||
// 为了安全起见，我们在后端应该加上 is_reasoning 标记，这里我们通过启发式或者统一拼接
|
||||
pushProgressLog(payload.content, payload.is_reasoning || false);
|
||||
}
|
||||
|
||||
if (payload.type === "final" && payload.content) {
|
||||
@@ -968,6 +980,15 @@ export function ChatInterface() {
|
||||
>
|
||||
{msg.role === "assistant" ? (
|
||||
<>
|
||||
{msg.reasoningContent && (
|
||||
<div className="mb-3 rounded-xl border border-zinc-200 bg-zinc-50/50 p-3 text-sm text-zinc-600 font-mono whitespace-pre-wrap leading-relaxed shadow-inner max-h-[300px] overflow-y-auto">
|
||||
<div className="flex items-center gap-2 mb-2 text-xs font-semibold text-zinc-500 uppercase tracking-wider">
|
||||
<Settings className={`h-3.5 w-3.5 ${msg.awaitingFirstToken ? 'animate-spin' : ''}`} />
|
||||
思考过程
|
||||
</div>
|
||||
{msg.reasoningContent}
|
||||
</div>
|
||||
)}
|
||||
{msg.progressLogs && msg.progressLogs.length > 0 ? (
|
||||
<div className="mb-2 rounded-xl border border-zinc-100 bg-zinc-50/70 px-3 py-2">
|
||||
<div className="flex items-center gap-2 text-zinc-500 text-xs">
|
||||
|
||||
Reference in New Issue
Block a user